The convergence of large language models and multimodal technologies is driving artificial intelligence to a new stage. By integrating multiple data types such as text, images, audio and video, this type of model enhances deep semantic understanding and cross-modal processing capabilities, so as to realize intelligent decision-making and interaction in more scenarios, showing the potential to evolve to general artificial intelligence. While laying the foundation for general artificial intelligence, multimodal large models also seek a balance between computational efficiency and generalization capabilities to meet the complex application needs of multiple fields such as medical and entertainment.
Multimodal large models play a key role in autonomous driving because of their ability to process and understand data from different information sources, such as visual images, radar data, LiDAR point clouds, sound, and other sensor inputs. Here are a few of the main applications of multimodal large models in autonomous driving:
Contextual Awareness and Understanding:
Multimodal large models can process multiple types of input data simultaneously, such as visual information captured by cameras and depth information provided by LiDAR, to more accurately identify and classify objects, such as pedestrians, other vehicles, traffic signs, etc., and understand the surrounding environment.
Decision Making:
By integrating information from different sensors, multimodal models can help autonomous driving systems make smarter driving decisions. This includes route planning, speed adjustment, and responding to unexpected situations such as sudden obstacles or complex traffic situations.
How to deal with long-tail scenarios:
One of the challenges for autonomous driving systems is to deal with so-called "long tail" scenarios – special situations that occur infrequently but are critical to safety. Multimodal large models can learn a large amount of training data to better understand and respond to these rare scenarios, improving the robustness and security of the system.
Figure: The main application of multi-modal large models in autonomous driving
AI Security Officer:
In some implementations, multimodal large models are used as AI safety officers to monitor the operation of unmanned vehicles in real time and provide an additional layer of safety. When encountering uncertain situations, AI safety officers can request the assistance of a human remote operator or give advice based on their own judgment to ensure safe driving.
Interaction & Communication:
Multimodal models can also facilitate effective communication between autonomous vehicles and other road users, such as communicating with pedestrians or other drivers through speech recognition and synthesis techniques, or communicating intent through visual signals.
Data-Driven Optimization:
Using large-scale datasets and advanced algorithms, multimodal large models can continuously learn and improve to continuously improve the performance of autonomous driving systems and adapt to changing road conditions and traffic rules.
The four basic capabilities have become the key to moving towards general artificial intelligence
Comprehension, generation, logic, and memory are the four basic capabilities of generative AI models. In terms of comprehension ability, AGI is able to perceive the world through multiple sensory inputs such as vision and hearing, and effectively understand and process this information. This means not only being able to recognize basic elements such as objects and sounds, but also understanding their meaning in a particular context, such as recognizing a person's face while understanding the person's emotional state or intention. In terms of generation capabilities, using advanced generation technology, large models can create high-quality content, from text to images to audio and video, showing strong creative potential and providing users with an unprecedented interactive experience. In terms of logic and memory, reasoning ability enables AGI to make logical inferences based on existing knowledge, solve complex problems, and make sound decisions. This includes, but is not limited to, deductive reasoning (deriving specific examples from general principles), inductive reasoning (generalizing general laws from specific examples), and retrospective reasoning (inferring causes from results). A good decision-making mechanism can enable AGI to find the optimal solution or satisfactory solution in uncertainty and change.
With these foundational capabilities, AGI is able to exhibit human-like intelligent behaviors in a wide range of domains, from simple to complex, and complete a wide range of tasks. However, achieving true AGI remains a huge challenge, and current research and development is still exploring how to build these capabilities more effectively.
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